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New course: Building Coding Agents with Tool Execution, taught by Tereza Tizkova and Fra from E2B. Most AI agents are limited to predefined function calls. This short course teaches you to build agents that write and execute code to accomplish tasks, accessing entire programming language ecosystems instead of being...

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Andrew Ng

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204,552 просмотров • 11 месяцев назад •via X (Twitter)

ОбразованиеНаука и технологии
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Комментарии: 34

Фото профиля Tereza Tizkova
Tereza Tizkova11 месяцев назад

@FraZuppichini @e2b It was great to work on this with @DeepLearningAI team 🫡🙏

Фото профиля Aish
Aish11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b why do both of them feel like AI generated

Фото профиля Akash Basudevan
Akash Basudevan11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Is it just me or the jaw lines are actually distracting here lol

Фото профиля Yacine Mahdid
Yacine Mahdid11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b I fucking love these

Фото профиля luu
luu11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Are two people on the left robots? They look very human, but sees like they still need some more work

Фото профиля E2B
E2B11 месяцев назад

@tereza_tizkova @FraZuppichini 🫡

Фото профиля Thomas Bustos
Thomas Bustos11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b @FraZuppichini let's gooooo 🚀

Фото профиля Henry Ogedegbe Jr.
Henry Ogedegbe Jr.11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b we love @tereza_tizkova 🙌🏿

Фото профиля theraptorlake_
theraptorlake_11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Always cool to see free courses, they help new students a lot..

Фото профиля Oscar Davies Writing on AI
Oscar Davies Writing on AI11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b There won't ever be AGI though

Фото профиля Jay_speaks
Jay_speaks11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Your courses are great recently was going through the maths specialisation course it clears all the concepts and develops deep understanding and intuition regarding the concepts

Фото профиля Sree Teja Laveti
Sree Teja Laveti11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b @grok is it only me or did someone else also notice that Andrew Ng wears this same shirt in more that 100 videos???

Фото профиля Krish Dasgupta
Krish Dasgupta11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Amazing !!!!! @tereza_tizkova - super happy to see this !!! Congratulation team @e2b

Фото профиля Sridhar A
Sridhar A11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b coding agents get interesting when they stop being toy demos and start handling real-world error paths, file ops and recovery loops. curious to see how this course frames the safety tradeoffs as autonomy increases.

Фото профиля Vincent Valentine
Vincent Valentine11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b this sounds like an exciting opportunity to deepen our understanding of ai agents.

Фото профиля Nitin Kaithvar
Nitin Kaithvar11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Powerful agents! The future of automation!

Фото профиля Pratap
Pratap11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b This is the key unlock! Agents that can call REAL tools vs pre-defined functions changes everything. It's the difference between a calculator and an actual problem-solver. Andrew dropping knowledge bombs. 💣

Фото профиля Marco Franzon
Marco Franzon11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Bro @FraZuppichini ❤️

Фото профиля Rithesh
Rithesh11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b course for future of coding agents that adapt and evolve on their own

Фото профиля 小北
小北11 месяцев назад

This course looks incredibly practical! The shift from giving agents fixed tools to letting them autonomously write and execute code is a total game-changer for the future of development. Love the thoughtful sandbox design too — makes it safe to go wild with experiments. Just signed up and can’t wait to build a Pandas-powered data analyst agent! 🔥 Thank you @AndrewYNg @tereza_tizkova @FraZuppichini for sharing! 🚀

Фото профиля Dr. Mohammed Lubbad | د. محمد لبد
Dr. Mohammed Lubbad | د. محمد لبد11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b The ability to execute code significantly enhances AI capabilities. How far could innovation extend with these skills? 🤖 #AIAdvancements

Фото профиля WellnessCoreAI
WellnessCoreAI10 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Building a full-stack agent that handles its own error loops? Sign me up. 🙋‍♂️ This covers exactly the skills the industry needs right now.

Фото профиля A–𝕏 (Full twist 😜)
A–𝕏 (Full twist 😜)11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b So @grok the end of the programming industry is concluded now. Do the authors and publishers survive in this Era? Limitation of knowledge may lead to loss of discoveries too! Remeber Bro......

Фото профиля Spiros Margaris
Spiros Margaris11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Great opportunity to learn.

Фото профиля Lucas Cooper-Bey
Lucas Cooper-Bey11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Taking it now. How exciting!!! And lol these were the biggest difficulties my project is suffering. How literally perfect timing. Amazing. Thank you.

Фото профиля tracey te braake
tracey te braake11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b This looks fascinating! The shift from predefined functions to code-writing agents is such a game changer. Thanks for making these resources accessible - excited to dive into this course!

Фото профиля Chenggay
Chenggay11 месяцев назад

Coding agents that write and execute their own code in safe sandboxes is the quiet leap from "demo" to "deployable". That’s when the barrier drops, not just for building, but for trusting them with real stakes like on-chain tasks. Feels like the agent series just got its biggest production boost yet. (White cards have been mapping this evolution if you're following along.)

Фото профиля Alin
Alin11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Building agents that can actually write and execute code is a game changer. The gap between chat-based AI and code-executing agents is massive. Great to see Andrew and the team teaching this - it's a skill everyone needs to learn

Фото профиля Pranay Mishra
Pranay Mishra11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b love this!!

Фото профиля Jacek (Jomsborg.eth)
Jacek (Jomsborg.eth)11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Great share

Фото профиля Ava Grace
Ava Grace11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Exciting opportunity to build coding agents with tool execution! Sounds innovative and hands-on.

Фото профиля Shobhit Khurana
Shobhit Khurana11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b On it

Фото профиля Himanshu Kumar
Himanshu Kumar11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Andrew, this sounds like a game changer, but will it be accessible to everyone, you think?

Фото профиля Anthara Fairooz
Anthara Fairooz11 месяцев назад

@tereza_tizkova @FraZuppichini @e2b Exciting course! Learning to build agents that write & execute code autonomously opens up endless possibilities.

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New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!
2:39

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!

Andrew Ng

350,175 просмотров • 2 лет назад

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:
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New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,894 просмотров • 1 год назад

🧲 Meet Magnet — the AI workspace for agentic coding. Build software with Claude Code, Cursor, Codex & co. → Write a ticket → Magnet assembles context → Fires to Claude Code, Deep Research, and other agent sessions in parallel Git worktree sandboxes → PRs appear → Use agents to help you spec, research, and decompose tasks Let your agents cook!
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🧲 Meet Magnet — the AI workspace for agentic coding. Build software with Claude Code, Cursor, Codex & co. → Write a ticket → Magnet assembles context → Fires to Claude Code, Deep Research, and other agent sessions in parallel Git worktree sandboxes → PRs appear → Use agents to help you spec, research, and decompose tasks Let your agents cook!

Nicolae Rusan

16,687 просмотров • 1 год назад

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:
1:48

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

Andrew Ng

82,067 просмотров • 1 год назад

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:
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New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

Andrew Ng

186,182 просмотров • 1 год назад

Introducing multi-agent orchestration in Oz, with support for Claude Code, Codex and the Warp Agent. Use /orchestrate to delegate complex tasks across a team of agents, locally or in the cloud.
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Introducing multi-agent orchestration in Oz, with support for Claude Code, Codex and the Warp Agent. Use /orchestrate to delegate complex tasks across a team of agents, locally or in the cloud.

Warp

1,198,899 просмотров • 5 месяцев назад